Senior AI Engineer

Hybrid working, strong total rewards, and a role focused on delivering production-grade AI that teams can actually reuse. Janus Henderson is building a firm-wide AI transformation to become the most technologically sophisticated asset manager in the industry, and the AI Technology function is responsible for building, governing, and operating the software behind it.

Your opportunity

As a Senior Applied AI Engineer (onsite in Denver, CO), you will lead delivery of major parts of the AI Engineering estate under the Head of AI, working with a centralized AI capability. AI Engineering is the core product and platform engineering team, building shared enterprise products such as Nexus (an agentic workspace), Accio (a centralized MCP server), and the orchestration, evaluation, and observability services beneath Libros and PRISM (projects delivered with Percepta).

You will take initiatives from evaluation and AI governance checkpoints into production, then own ongoing operation once live. The role also includes end-to-end build of targeted business applications for named business owners, with a first focus on Distribution, followed by areas such as trading, investment risk, client servicing, and operations. You will drive deliberate SaaS decommissioning where in-house builds can replace overlapping subscriptions, consolidating onto governed, in-house capability.

What you’ll do

• Design, build, and productionise AI products and shared platform services, owning a workstream end to end through live running.
• Build for reuse by turning proven product capability into shared components, libraries, skills, tools, MCP servers, and connectors.
• Implement infrastructure as code, own CI/CD pipelines, and accept services into operation once ownership, controls, and support are clear.
• Investigate incidents and defects in your owned services, lead recovery, carry fixes to root cause, and act as L3 escalation.
• Build agentic applications and workflows covering agent design, prompt and context engineering, tool use, memory, retrieval, and human-in-the-loop controls.
• Implement model selection, routing, and fallback through the model gateway, handling provider change while preserving capability, cost, and behavioral differences.
• Build the orchestration layer long-running and multi-step agents depend on, including permissions, workload isolation, safe execution, and the applications built on top.
• Test claims for new models, frameworks, and patterns before recommending adoption.
• Build governed AI-ready views, indexes, semantic context, and connectors over Snowflake, enterprise platforms, APIs, and external providers.
• Extend Accio so new datasets and downstream MCP servers are reachable through a single governed interface rather than one-off integrations.
• Build ingestion pipelines and data models where source data does not arrive usable, while keeping canonical source ownership with the relevant Technology team.
• Own data lineage, quality, and freshness, and implement least-privilege access for users, agents, tools, and service identities.
• Take targeted business applications through evaluation, controls, and release evidence aligned to shared products, and own support and lifecycle once live.
• Build evaluation and regression suites for models, prompts, agents, and platform changes; meet agreed thresholds and instrument quality, safety, reliability, latency, drift, usage, and cost.
• Implement AI Governance Implementation and AI Security controls as code with secure defaults, producing release evidence proportionate to risk and generated by the platform.
• Work alongside Percepta engineers on Libros, PRISM, and the wider estate to ensure maintainability and adherence to engineering standards.
• Mentor AI Engineers through pairing, design discussion, and code review, and help establish engineering standards and an agentic SDLC.
• Partner with AI Architecture and Forward Deployed Engineering so reference patterns become supported shared capability, and feed adoption data into the backlog.

What you bring

• At least 6 years in software, data, or platform engineering, with a track record of shipping and operating production systems.
• Production experience with LLM applications and agentic systems, including prompt and context engineering, agent development, retrieval, tool use, evaluation, and scalable deployment.
• Strong Python and SQL with judgement to write maintainable code.
• Hands-on experience building agentic capability such as MCP servers, tools, skills, or connectors.
• Hands-on experience with a major cloud, ideally Azure, including containers or serverless compute, infrastructure as code, CI/CD, identity, RBAC, and secret management.
• Experience owning services in production, including monitoring, incident investigation, upgrades, lifecycle management, and establishing evaluation and observability for AI systems.
• Good judgement in a regulated environment, translating security, privacy, risk, and audit requirements into technical controls.
• Experience mentoring engineers and leading technical work without relying on reporting authority, plus clear communication with engineers, control functions, and business stakeholders.

Technologies you’ll work with

• Python, SQL, LLMs, agent frameworks, MCP
• Azure AI Foundry, Snowflake, Microsoft Fabric, Azure
• Terraform, Docker, CI/CD

Benefits

• Hybrid working and reasonable accommodations
• Generous Holiday policies
• Excellent Health and Wellbeing benefits including corporate membership to Wellhub
• Paid volunteer time
• Support for professional development courses and tuition/qualification reimbursement
• Maternal/paternal leave benefits and family services
• Employee events and programs including a 14er challenge
• Complimentary beverages, snacks, and all-employee Happy Hours
• Position may be eligible for an annual discretionary bonus award from the profit pool
• Comprehensive total rewards including competitive compensation, pension/retirement plans, and various health, wellbeing, and lifestyle benefits

Compensation

$130,000 - $220,000 base salary range (estimated). Actual pay may be different. This position may be open through [add date]. Colorado law requires an estimated closing date for job postings.